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1.
Lease, Matthew A.
Beyond keywords: finding information more accurately and
easily using natural language.
Degree: PhD, Computer Science, 2009, Brown University
URL: https://repository.library.brown.edu/studio/item/bdr:206/
► Information retrieval (IR) has become a ubiquitous technology for quickly and easily finding information on a given topic amidst the wealth of digital content available…
(more)
▼ Information retrieval (IR) has become a ubiquitous
technology for quickly and easily finding information on a given
topic amidst the wealth of digital content available today. This
dissertation addresses search for written and spoken natural
language documents, including news articles, Web pages, and spoken
interviews. Effective model estimation is identified as a key
problem, and several novel estimation techniques are presented and
shown to significantly enhance search accuracy. While search is
typically performed via a few carefully chosen keywords,
formulating effective keyword queries is often unintuitive and
iterative, particularly when seeking complex information. As an
alternative to keyword search, this dissertation investigates
search using ``natural'' queries, such as questions or sentences a
person might naturally articulate in communicating their
information need to another person. By moving toward supporting
natural queries, the communication burden is shifted from user
query formulation to system interpretation of natural language. The
challenge in enacting such a shift is enabling automatic IR systems
to more effectively cope with natural language. To this end,
several new estimation techniques for modeling natural queries are
described. In comparison to a maximum likelihood baseline, 15-20%
relative improvement in mean average precision (MAP) is
demonstrated without use of query expansion. When an IR system
discovers or is provided one or more feedback documents
exemplifying a user's information need, there is further
opportunity to improve search accuracy by exploiting document
contents for query expansion. However, since documents typically
discuss multiple topics varying in importance and relevance to any
information need, the system must again be able to effectively
interpret verbose natural language. Consequently, an estimation
method for leveraging such documents is presented and shown to
yield state-of-the-art search accuracy. Depending on the base model
employed, 15-85% relative MAP improvement is achieved. When
modeling higher-order lexical features or searching smaller
document collections like cultural history archives, sparsity
become particularly problematic for estimation. To cope with such
sparsity, additional estimation methods are described which yield
5-20% relative improvement in MAP accuracy across varying
conditions of query verbosity.
Advisors/Committee Members: Charniak, Eugene (director), Johnson, Mark (reader), Allan, James (reader).
Subjects/Keywords: supervised learning
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APA ·
Chicago ·
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APA (6th Edition):
Lease, M. A. (2009). Beyond keywords: finding information more accurately and
easily using natural language. (Doctoral Dissertation). Brown University. Retrieved from https://repository.library.brown.edu/studio/item/bdr:206/
Chicago Manual of Style (16th Edition):
Lease, Matthew A. “Beyond keywords: finding information more accurately and
easily using natural language.” 2009. Doctoral Dissertation, Brown University. Accessed January 17, 2021.
https://repository.library.brown.edu/studio/item/bdr:206/.
MLA Handbook (7th Edition):
Lease, Matthew A. “Beyond keywords: finding information more accurately and
easily using natural language.” 2009. Web. 17 Jan 2021.
Vancouver:
Lease MA. Beyond keywords: finding information more accurately and
easily using natural language. [Internet] [Doctoral dissertation]. Brown University; 2009. [cited 2021 Jan 17].
Available from: https://repository.library.brown.edu/studio/item/bdr:206/.
Council of Science Editors:
Lease MA. Beyond keywords: finding information more accurately and
easily using natural language. [Doctoral Dissertation]. Brown University; 2009. Available from: https://repository.library.brown.edu/studio/item/bdr:206/

University of Illinois – Chicago
2.
Mohammadi, Neshat.
Supervised Tensor Learning with Applications.
Degree: 2017, University of Illinois – Chicago
URL: http://hdl.handle.net/10027/22099
► In this thesis, a new supervised tensor learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based…
(more)
▼ In this thesis, a new
supervised tensor
learning (STL) approach with application to neuroimages has been studied and implemented. We applied our proposed polynomial kernel-based approach in order to analyze HIV infections based on fMRI and DTI brain images. The goal of this project was to achieve a more accurate prediction for HIV diagnosis using fMRI and DTI images of the brain.
To achieve this goal we tried to improve the accuracy of the STL model by directly using tensor data as an input. Then, in order to solve STL problems, a structure-preserving feature mapping in addition to CP decomposed results has been defined to derive a Dual Structure-preserving Kernel (DuSK) in the tensor product feature space. Broadly, DuSK is a general framework to convert any vector-based kernel function to an equivalent tensorial representation.
Different from traditional STL frameworks, that usually intend to use linear models, our approach was based on a nonlinear kernel method and tensor factorization techniques that can preserve the multi- way structures of tensorial data. We investigated the performance of DuSK together with Support Vector Machine (SVM) for HIV infection classification on tensorial fMRI and matricized DTI data sets.
The experimental results are presented with details in evaluation chapter. According to our experiments, that DuSK with a nonlinear kernel can effectively boost classification performance in HIV data sets, and the choice of optimal kernel depends on the nature of the input data. Specifically, DuSK with an RBF kernel performs better on fMRI data, while DuSK with a polynomial kernel is better for DTI data.
Advisors/Committee Members: Yu, Philip S. (advisor), Smida, Besma (committee member), Sharabiani, Ashkan (committee member), Yu, Philip S. (chair).
Subjects/Keywords: Supervised Tensor Learning
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❌
APA ·
Chicago ·
MLA ·
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CSE |
Export
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APA (6th Edition):
Mohammadi, N. (2017). Supervised Tensor Learning with Applications. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/22099
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Thesis, University of Illinois – Chicago. Accessed January 17, 2021.
http://hdl.handle.net/10027/22099.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Mohammadi, Neshat. “Supervised Tensor Learning with Applications.” 2017. Web. 17 Jan 2021.
Vancouver:
Mohammadi N. Supervised Tensor Learning with Applications. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/10027/22099.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Mohammadi N. Supervised Tensor Learning with Applications. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/22099
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University
3.
Hao, Guohua.
Revisiting output coding for sequential supervised learning.
Degree: MS, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/10897
► Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training…
(more)
▼ Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this issue by converting a problem with many labels to a set of problems with binary labels. Models were independently trained for each binary problem, at a much reduced computational cost, and then combined for joint inference over the original labels. Here we revisit this idea and show through experiments on synthetic and benchmark data sets that the approach can perform poorly when it is critical to explicitly capture the Markovian transition structure of the large-label problem. We then describe a simple cascade-training approach and show that it can improve performance on such problems with negligible computational overhead.
Advisors/Committee Members: Fern, Alan (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: ECOC; Supervised learning (Machine learning)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hao, G. (2009). Revisiting output coding for sequential supervised learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10897
Chicago Manual of Style (16th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Masters Thesis, Oregon State University. Accessed January 17, 2021.
http://hdl.handle.net/1957/10897.
MLA Handbook (7th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 17 Jan 2021.
Vancouver:
Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/1957/10897.
Council of Science Editors:
Hao G. Revisiting output coding for sequential supervised learning. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10897

Rutgers University
4.
Gazzola, Gianluca.
Supervised learning methods for variable importance and regression with uncertainty on dependent data.
Degree: PhD, Operations Research, 2019, Rutgers University
URL: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
► This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of…
(more)
▼ This dissertation covers a collection of supervised learning methods targeted to data with complex dependence patterns. Part of our work orbits around the concept of variable importance, that is, the relative contribution an input variable to the prediction or the explanation of an output variable. Our interest in variable importance, and its estimation, is two-fold. On the one hand, as a tool for the characterization of data sets produced by multi-stage systems, where variables are related to each other via a network of correlations and causal dependencies. On the other hand, as a tool for the selection of minimal input-variable subsets with optimal predictive performance, in a more general framework involving data sets with an interesting structure of inter-variable dependence and redundancy. The rest of our work focuses on the problem of function approximation in the presence of uncertainty, and, specifically, on the calculation of optimal interpolating hyperplanes from data represented by convex polyhedra, rather than points. In this context, we propose algorithms to determine the spatial orientation of such polyhedra based on the multivariate relationships observed in the data, with particular focus on missing-value scenarios. For all of our methods, we present successful validation on an extensive and diverse array of real-world and simulated problems.
Advisors/Committee Members: Jeong, Myong K (chair), Boros, Endre (internal member), BEN-ISRAEL, ADI (internal member), Packard, Norman (outside member), CHAOVALITWONGSE, W. ART (outside member), Tortorella, Michael (outside member), School of Graduate Studies.
Subjects/Keywords: Supervised learning (Machine learning)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Gazzola, G. (2019). Supervised learning methods for variable importance and regression with uncertainty on dependent data. (Doctoral Dissertation). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
Chicago Manual of Style (16th Edition):
Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Doctoral Dissertation, Rutgers University. Accessed January 17, 2021.
https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.
MLA Handbook (7th Edition):
Gazzola, Gianluca. “Supervised learning methods for variable importance and regression with uncertainty on dependent data.” 2019. Web. 17 Jan 2021.
Vancouver:
Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Internet] [Doctoral dissertation]. Rutgers University; 2019. [cited 2021 Jan 17].
Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/.
Council of Science Editors:
Gazzola G. Supervised learning methods for variable importance and regression with uncertainty on dependent data. [Doctoral Dissertation]. Rutgers University; 2019. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/60158/
5.
Aversano, Gianmarco.
Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.
Degree: Docteur es, Combustion, 2019, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....)
URL: http://www.theses.fr/2019SACLC095
► L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées…
(more)
▼ L’objectif final étant de développer des modèles d’ordre réduit pour les applications de combustion, des techniques d’apprentissage automatique non supervisées et supervisées ont été testées et combinées dans les travaux de la présente thèse pour l’extraction de caractéristiques et la construction de modèles d’ordre réduit. Ainsi, l’application de techniques pilotées par les données pour la détection des caractéristiques d’ensembles de données de combustion turbulente (simulation numérique directe) a été étudiée sur deux flammes H2 / CO: une évolution spatiale (DNS1) et une jet à évolution temporelle (DNS2). Des méthodes telles que l’analyse en composantes principales (ACP), l’analyse en composantes principales locales (LPCA), la factorisation matricielle non négative (NMF) et les autoencodeurs ont été explorées à cette fin. Il a été démontré que divers facteurs pouvaient affecter les performances de ces méthodes, tels que les critères utilisés pour le centrage et la mise à l’échelle des données d’origine ou le choix du nombre de dimensions dans les approximations de rang inférieur. Un ensemble de lignes directrices a été présenté qui peut aider le processus d’identification de caractéristiques physiques significatives à partir de données de flux réactifs turbulents. Des méthodes de compression de données telles que l’analyse en composantes principales (ACP) et les variations ont été combinées à des méthodes d’interpolation telles que le krigeage, pour la construction de modèles ordonnées à prix réduits et calculables pour la prédiction de l’état d’un système de combustion dans des conditions de fonctionnement inconnues ou des combinaisons de modèles valeurs de paramètre d’entrée. La méthodologie a d’abord été testée pour la prévision des flammes 1D avec un nombre croissant de paramètres d’entrée (rapport d’équivalence, composition du carburant et température d’entrée), avec des variantes de l’approche PCA classique, à savoir PCA contrainte et PCA locale, appliquée aux cas de combustion la première fois en combinaison avec une technique d’interpolation. Les résultats positifs de l’étude ont conduit à l’application de la méthodologie proposée aux flammes 2D avec deux paramètres d’entrée, à savoir la composition du combustible et la vitesse d’entrée, qui ont donné des résultats satisfaisants. Des alternatives aux méthodes non supervisées et supervisées choisies ont également été testées sur les mêmes données 2D. L’utilisation de la factorisation matricielle non négative (FNM) pour l’approximation de bas rang a été étudiée en raison de la capacité de la méthode à représenter des données à valeur positive, ce qui permet de ne pas enfreindre des lois physiques importantes telles que la positivité des fractions de masse d’espèces chimiques et comparée à la PCA. Comme méthodes supervisées alternatives, la combinaison de l’expansion du chaos polynomial (PCE) et du Kriging et l’utilisation de réseaux de neurones artificiels (RNA) ont été testées. Les résultats des travaux susmentionnés ont ouvert la voie au développement d’un…
Advisors/Committee Members: Gicquel, Olivier (thesis director), Parente, Alessandro (thesis director).
Subjects/Keywords: Combustion; Unsupervised learning; Supervised learning; Combustion; Unsupervised learning; Supervised learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Aversano, G. (2019). Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. (Doctoral Dissertation). Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Retrieved from http://www.theses.fr/2019SACLC095
Chicago Manual of Style (16th Edition):
Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Doctoral Dissertation, Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....). Accessed January 17, 2021.
http://www.theses.fr/2019SACLC095.
MLA Handbook (7th Edition):
Aversano, Gianmarco. “Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif.” 2019. Web. 17 Jan 2021.
Vancouver:
Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. [cited 2021 Jan 17].
Available from: http://www.theses.fr/2019SACLC095.
Council of Science Editors:
Aversano G. Development of physics-based reduced-order models for reacting flow applications : Développement de modèles d’ordre réduit basés sur la physique pour les applications d’écoulement réactif. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); Université libre de Bruxelles (1970-....); 2019. Available from: http://www.theses.fr/2019SACLC095

University of Adelaide
6.
Shen, Tong.
Context Learning and Weakly Supervised Learning for Semantic Segmentation.
Degree: 2018, University of Adelaide
URL: http://hdl.handle.net/2440/120354
► This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel…
(more)
▼ This thesis focuses on one of the fundamental problems in computer vision, semantic segmentation, whose task is to predict a semantic label for each pixel of an image. Although semantic segmentation models have been largely improved thanks to the great representative power of deep
learning techniques, there are still open questions needed to be discussed. In this thesis, we discuss two problems regarding semantic segmentation, scene consistency and weakly
supervised segmentation. In the first part of the thesis, we discuss the issue of scene consistency in semantic segmentation. This issue comes from the fact that trained models sometimes produce noisy and implausible predictions that are not semantically consistent with the scene or context. By explicitly considering scene consistency both locally and globally, we can narrow down the possible categories for each pixel and generate the desired prediction more easily. In the thesis, we address this issue by introducing a dense multi-label module. In general, multi-label classification refers to the task of assigning multiple labels to a given image. We extend the idea to different levels of the image, and assign multiple labels to different regions of the image. Dense multi-label acts as a constraint to encourage scene consistency locally and globally. For dense prediction problems such as semantic segmentation, training a model requires densely annotated data as ground-truth, which involves a great amount of human annotation effort and is very time-consuming. Therefore, it is worth investigating semi- or weakly
supervised methods that require much less supervision. Particularly, weakly
supervised segmentation refers to training the model using only image-level labels, while semi-
supervised segmentation refers to using partially annotated data or a small portion of fully annotated data to train. In the thesis, two weakly
supervised methods are proposed where only image-level labels are required. The two methods share some similar motivations. First of all, since pixel-level masks are missing in this particular setting, the two methods are all designed to estimate the missing ground-truth and further use them as pseudo ground-truth for training. Secondly, they both use data retrieved from the internet as auxiliary data because web data are cheap to obtain and exist in a large amount. Although there are similarities between these two methods, they are designed from different perspectives. The motivation for the first method is that given a group of images crawled from the internet that belong to the same semantic category, it is a good choice to use co-segmentation to extract the masks of them, which gives us almost free pixel-wise training samples. Those internet images along with the extracted masks are used to train a mask generator to help us estimate the pseudo ground-truth for the training images. The second method is designed as a bi-directional framework between the target domain and the web domain. The term “bi-directional” refers to the concept that the…
Advisors/Committee Members: Shen, Chunhua (advisor), School of Computer Science (school).
Subjects/Keywords: weakly supervised learning; semantic segmentation
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Shen, T. (2018). Context Learning and Weakly Supervised Learning for Semantic Segmentation. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/120354
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Thesis, University of Adelaide. Accessed January 17, 2021.
http://hdl.handle.net/2440/120354.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shen, Tong. “Context Learning and Weakly Supervised Learning for Semantic Segmentation.” 2018. Web. 17 Jan 2021.
Vancouver:
Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Internet] [Thesis]. University of Adelaide; 2018. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/2440/120354.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shen T. Context Learning and Weakly Supervised Learning for Semantic Segmentation. [Thesis]. University of Adelaide; 2018. Available from: http://hdl.handle.net/2440/120354
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology
7.
Van Hecke, K.G. (author).
Persistent self-supervised learning principle: Study and demonstration on flying robots.
Degree: 2015, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd
► We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised…
(more)
▼ We introduce, study and demonstrate Persistent Self-Supervised Learning (PSSL), a machine learning method for usage onboard robotic platforms. The PSSL model leverages a standard supervised learning method to simplify the learning problem, but acquires training data in an unsupervised and autonomous manner. Using two platforms, a small multicopter on earth and the space based test bed SPHERES inside the International Space Station , we demonstrate the PSSL principle on a proof of concept problem: learning monocular depth estimation using stereo vision. The robot operates first in a ground truth mode based on the distance perceived by the stereo system, while persistently learning the environment using monocular cues. After the performance of the estimator transcends a ROC quality measure, the robot switches to operation based on the monocular depth estimates. Our results show the viability of the PSSL method, by being able to navigate a room on the basis of learned monocular vision, without collecting any training data beforehand. We identify a major challenge in PSSL caused by a training bias due to behavioral differences in the estimator and the ground truth based operation; however, this is a known problem also for related learning methods such as reinforcement learning. PSSL helps solve this problem by 1) clearly separating the learning problem from the behavior and 2) the possibility to keep learning during estimator behavior.
Pattern recognition
Embedded Systems
Electrical Engineering, Mathematics and Computer Science
Advisors/Committee Members: De Croon, G.C.H.E. (mentor), Van der Maaten, L.J.P. (mentor), Izzo, D. (mentor), Hennes, D. (mentor).
Subjects/Keywords: persistent self-supervised learning; MAV
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Van Hecke, K. G. (. (2015). Persistent self-supervised learning principle: Study and demonstration on flying robots. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd
Chicago Manual of Style (16th Edition):
Van Hecke, K G (author). “Persistent self-supervised learning principle: Study and demonstration on flying robots.” 2015. Masters Thesis, Delft University of Technology. Accessed January 17, 2021.
http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.
MLA Handbook (7th Edition):
Van Hecke, K G (author). “Persistent self-supervised learning principle: Study and demonstration on flying robots.” 2015. Web. 17 Jan 2021.
Vancouver:
Van Hecke KG(. Persistent self-supervised learning principle: Study and demonstration on flying robots. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2021 Jan 17].
Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd.
Council of Science Editors:
Van Hecke KG(. Persistent self-supervised learning principle: Study and demonstration on flying robots. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:b722da02-089f-42a8-a3ea-fb3f5900bcdd

Colorado School of Mines
8.
Jackson, Ryan Blake.
Machine learning for encrypted Amazon Echo traffic classification.
Degree: MS(M.S.), Computer Science, 2018, Colorado School of Mines
URL: http://hdl.handle.net/11124/172223
► As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine learning…
(more)
▼ As smart speakers like the Amazon Echo become more popular, they have given rise to rampant concerns regarding user privacy. This work investigates machine
learning techniques to extract ostensibly private information from the TCP traffic moving between an Echo device and Amazon's servers, despite the fact that all such traffic is encrypted. Specifically, we investigate two
supervised classification problems using six machine
learning algorithms and three feature vectors. The "request type classification" problem seeks to determine what type of user request is being answered by the Echo. With six classes, we achieve 97% accuracy in this task using random forests. The "speaker identification" problem seeks to determine who, of a finite set of possible speakers, is speaking to the Echo. In this task, with two classes, we outperform random guessing by a small but statistically significant margin with an accuracy of 58%. We discuss the reasons for, and implications of, these results, and suggest several avenues for future research in this domain.
Advisors/Committee Members: Camp, Tracy (advisor), Wang, Hua (committee member), Schurgot, Mary (committee member).
Subjects/Keywords: supervised classification; machine learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jackson, R. B. (2018). Machine learning for encrypted Amazon Echo traffic classification. (Masters Thesis). Colorado School of Mines. Retrieved from http://hdl.handle.net/11124/172223
Chicago Manual of Style (16th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Masters Thesis, Colorado School of Mines. Accessed January 17, 2021.
http://hdl.handle.net/11124/172223.
MLA Handbook (7th Edition):
Jackson, Ryan Blake. “Machine learning for encrypted Amazon Echo traffic classification.” 2018. Web. 17 Jan 2021.
Vancouver:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Internet] [Masters thesis]. Colorado School of Mines; 2018. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/11124/172223.
Council of Science Editors:
Jackson RB. Machine learning for encrypted Amazon Echo traffic classification. [Masters Thesis]. Colorado School of Mines; 2018. Available from: http://hdl.handle.net/11124/172223

Oklahoma State University
9.
Spain, Marc.
Study on log event noise reduction by using Naive Bayes supervised machine learning.
Degree: Computer Science, 2019, Oklahoma State University
URL: http://hdl.handle.net/11244/324912
► This research addresses which Naive Bayes model would be best to predict Windows log events that could be considered noise or in other words not…
(more)
▼ This research addresses which Naive Bayes model would be best to predict Windows log events that could be considered noise or in other words not containing information about malicious activities. With the exploding amount of log data being generated by servers, large corporations or organizations are having an increasingly difficult time analyzing these logs to find evidence of malicious activity in their environment. Fortune 200 and larger corporations today are producing Terabytes of log events daily and this is expanding at a rate that soon it will be in the Petabytes. It is estimated that 80 to 90 percent of these log events could be classified as noise or just informational. They are not needed for finding evidence of malicious activity. By showing a process that can be used to predict whether these log events are noise or non-noise, with a reasonable degree of accuracy, tools could then be used to analyze log events to find malicious activity to filter out noise events and reduce the amount of data needed to be processed. This research will compare the Naive Bayes Bag of Words Multinomial, Multinomial TF-IDF and Multi-Variate Bernoulli models using different size feature word sets in predicting Windows noise log events.
Advisors/Committee Members: Park, Nohpill (advisor), Crick, Chris (committee member), Akbas, Esra (committee member).
Subjects/Keywords: naive bayes; supervised machine learning
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Spain, M. (2019). Study on log event noise reduction by using Naive Bayes supervised machine learning. (Thesis). Oklahoma State University. Retrieved from http://hdl.handle.net/11244/324912
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Spain, Marc. “Study on log event noise reduction by using Naive Bayes supervised machine learning.” 2019. Thesis, Oklahoma State University. Accessed January 17, 2021.
http://hdl.handle.net/11244/324912.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Spain, Marc. “Study on log event noise reduction by using Naive Bayes supervised machine learning.” 2019. Web. 17 Jan 2021.
Vancouver:
Spain M. Study on log event noise reduction by using Naive Bayes supervised machine learning. [Internet] [Thesis]. Oklahoma State University; 2019. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/11244/324912.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Spain M. Study on log event noise reduction by using Naive Bayes supervised machine learning. [Thesis]. Oklahoma State University; 2019. Available from: http://hdl.handle.net/11244/324912
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Wayne State University
10.
Hailat, Zeyad.
Deep Learning Methods For Visual Object Recognition.
Degree: PhD, Computer Science, 2018, Wayne State University
URL: https://digitalcommons.wayne.edu/oa_dissertations/2026
► Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large…
(more)
▼ Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-
supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. We summarize our main contributions in three main facets describe below.
First, we presented anew Hybrid Residual Network Method (HyResNet) that exploits the power of both
supervised and unsupervised deep
learning methods into a single deep
supervised learning model. Our experiments show the efficacy of HyResNet on visual object recognition tasks. We tested HyResNet on benchmark datasets with various configurations and settings. HyResNet showed comparable results to the state-of-the-art methods on the benchmark datasets.
Second, we proposed a deep semi-
supervised learning method (DSSL). DSSL utilizes both
supervised and unsupervised neural networks. The novelty of DSSL originates from its nature in employing a limited number of labeled training examples in conjunction with sufficiently large unlabeled examples to create a classification model. The combination of DSSL architecture and self-training has a joint impact on the performance over the DSSL. We measured the performance of DSSL method on five benchmark datasets with various labeled / unlabeled levels of training examples and then compared our results with state-of-the-art methods. The experiments show that DSSL sets a new state-of-the-art record for various benchmark tasks.
Finally, we introduced a new teacher/student semi-
supervised deep
learning methods (TS-DSSL). TS-DSSL accepts an input of noisy label training dataset then it employs a self-training and self-cleansing techniques to train a deep
learning model. The integration of TS-DSSL architecture with the training protocol maintain the stability of the model and enhance the overall model performance. We evaluated the performance of TS-DSSL on benchmark semi-
supervised learning tasks with different levels of noisy labels synthesized from different noise distributions. The experiments showed that TS-DSSL sets a new state-of-the-art record on the benchmark tasks.
Advisors/Committee Members: XUEWEN CHEN.
Subjects/Keywords: artificial intelligence; deep learning; machine learning; self-learning; semi-supervised learning; supervised learning; Computer Sciences
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hailat, Z. (2018). Deep Learning Methods For Visual Object Recognition. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/2026
Chicago Manual of Style (16th Edition):
Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Doctoral Dissertation, Wayne State University. Accessed January 17, 2021.
https://digitalcommons.wayne.edu/oa_dissertations/2026.
MLA Handbook (7th Edition):
Hailat, Zeyad. “Deep Learning Methods For Visual Object Recognition.” 2018. Web. 17 Jan 2021.
Vancouver:
Hailat Z. Deep Learning Methods For Visual Object Recognition. [Internet] [Doctoral dissertation]. Wayne State University; 2018. [cited 2021 Jan 17].
Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026.
Council of Science Editors:
Hailat Z. Deep Learning Methods For Visual Object Recognition. [Doctoral Dissertation]. Wayne State University; 2018. Available from: https://digitalcommons.wayne.edu/oa_dissertations/2026

University of Sydney
11.
He, Fengxiang.
Instance-Dependent Positive-Unlabelled Learning
.
Degree: 2018, University of Sydney
URL: http://hdl.handle.net/2123/20115
► An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant…
(more)
▼ An emerging topic in machine learning is how to learn classifiers from datasets containing only positive and unlabelled examples (PU learning). This problem has significant importance in both academia and industry. This thesis addresses the PU learning problem following a natural strategy that treats unlabelled data as negative. By this way, a PU dataset is transferred to a fully-labelled dataset but with label noise. This strategy has been employed by many existing works and is usually called the one-side noise model. Under the framework of the one-side noise model, this thesis proposes an instance-dependent model to express how likely a negative label is corrupted. The model relies on the probabilistic gap, which is defined as the difference between the posteriors that an instance is respectively from the classes of positive or negative. Intuitively, the instance with a smaller probabilistic gap is more likely to be wrongly labelled. Motivated by this intuition, this thesis assumes there is a negative correlation between the noisy probability of the instance and the corresponding probabilistic gap. This model is named as probabilistic-gap PU model (PGPU model). Based on the PGPU model, this thesis designs Bayesian relabelling method that can select a group of the unlabelled instances and give them new labels that are identical to the ones assigned by a Bayesian optimal classifier. By this way, we can significantly extend the labelled dataset. Eventually, this thesis employs conventional binary classification methods to learn a classifier from the extended labelled datasets. It is worth noting that there could be a sub-domain of the instances where no data point can be relabelled. This issue could lead to a biased classifier. A kernel mean matching technique is then employed to remedy this problem. This thesis also evaluates the proposed method in both theoretical and empirical manners. Both theoretical and empirical results are in agreements with our method.
Subjects/Keywords: Postive-unlabelled learning;
weakly supervised learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
He, F. (2018). Instance-Dependent Positive-Unlabelled Learning
. (Thesis). University of Sydney. Retrieved from http://hdl.handle.net/2123/20115
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning
.” 2018. Thesis, University of Sydney. Accessed January 17, 2021.
http://hdl.handle.net/2123/20115.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
He, Fengxiang. “Instance-Dependent Positive-Unlabelled Learning
.” 2018. Web. 17 Jan 2021.
Vancouver:
He F. Instance-Dependent Positive-Unlabelled Learning
. [Internet] [Thesis]. University of Sydney; 2018. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/2123/20115.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
He F. Instance-Dependent Positive-Unlabelled Learning
. [Thesis]. University of Sydney; 2018. Available from: http://hdl.handle.net/2123/20115
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin
12.
Joshi, Shalmali Dilip.
Constraint based approaches to interpretable and semi-supervised machine learning.
Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/1259
► Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social…
(more)
▼ Interpretability and Explainability of machine
learning algorithms are becoming increasingly important as Machine
Learning (ML) systems get widely applied to domains like clinical healthcare, social media and governance. A related major challenge in deploying ML systems pertains to reliable
learning when expert annotation is severely limited. This dissertation prescribes a common framework to address these challenges, based on the use of constraints that can make an ML model more interpretable, lead to novel methods for explaining ML models, or help to learn reliably with limited supervision.
In particular, we focus on the class of latent variable models and develop a general
learning framework by constraining realizations of latent variables and/or model parameters. We propose specific constraints that can be used to develop identifiable latent variable models, that in turn learn interpretable outcomes. The proposed framework is first used in Non–negative Matrix Factorization and Probabilistic Graphical Models. For both models, algorithms are proposed to incorporate such constraints with seamless and tractable augmentation of the associated
learning and inference procedures. The utility of the proposed methods is demonstrated for our working application domain – identifiable phenotyping using Electronic Health Records (EHRs). Evaluation by domain experts reveals that the proposed models are indeed more clinically relevant (and hence more interpretable) than existing counterparts. The work also demonstrates that while there may be inherent trade–offs between constraining models to encourage interpretability, the quantitative performance of downstream tasks remains competitive.
We then focus on constraint based mechanisms to explain decisions or outcomes of
supervised black-box models. We propose an explanation model based on generating examples where the nature of the examples is constrained i.e. they have to be sampled from the underlying data domain. To do so, we train a generative model to characterize the data manifold in a high dimensional ambient space. Constrained sampling then allows us to generate naturalistic examples that lie along the data manifold. We propose ways to summarize model behavior using such constrained examples.
In the last part of the contributions, we argue that heterogeneity of data sources is useful in situations where very little to no supervision is available. This thesis leverages such heterogeneity (via constraints) for two critical but widely different machine
learning algorithms. In each case, a novel algorithm in the sub-class of co–regularization is developed to combine information from heterogeneous sources. Co–regularization is a framework of constraining latent variables and/or latent distributions in order to leverage heterogeneity. The proposed algorithms are utilized for clustering, where the intent is to generate a partition or grouping of observed samples, and for
Learning to Rank algorithms – used to rank a set of observed samples in order of preference with respect…
Advisors/Committee Members: Ghosh, Joydeep (advisor), Koyejo, Oluwasanmi (committee member), Sanghavi, Sujay (committee member), Vikalo, Haris (committee member), Mooney, Raymond (committee member).
Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259
Chicago Manual of Style (16th Edition):
Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed January 17, 2021.
http://dx.doi.org/10.26153/tsw/1259.
MLA Handbook (7th Edition):
Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 17 Jan 2021.
Vancouver:
Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2021 Jan 17].
Available from: http://dx.doi.org/10.26153/tsw/1259.
Council of Science Editors:
Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259
13.
Byun, Byungki.
On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.
Degree: PhD, Electrical and Computer Engineering, 2012, Georgia Tech
URL: http://hdl.handle.net/1853/43597
► This dissertation presents the development of a semi-supervised incremental learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having…
(more)
▼ This dissertation presents the development of a semi-
supervised incremental
learning framework with a multi-view perspective for image concept modeling. For reliable image concept characterization, having a large number of labeled images is crucial. However, the size of the training set is often limited due to the cost required for generating concept labels associated with objects in a large quantity of images. To address this issue, in this research, we propose to incrementally incorporate unlabeled samples into a
learning process to enhance concept models originally learned with a small number of labeled samples. To tackle the sub-optimality problem of conventional techniques, the proposed incremental
learning framework selects unlabeled samples based on an expected error reduction function that measures contributions of the unlabeled samples based on their ability to increase the modeling accuracy. To improve the convergence property of the proposed incremental
learning framework, we further propose a multi-view
learning approach that makes use of multiple features such as color, texture, etc., of images when including unlabeled samples. For robustness to mismatches between training and testing conditions, a discriminative
learning algorithm, namely a kernelized maximal- figure-of-merit (kMFoM)
learning approach is also developed. Combining individual techniques, we conduct a set of experiments on various image concept modeling problems, such as handwritten digit recognition, object recognition, and image spam detection to highlight the effectiveness of the proposed framework.
Advisors/Committee Members: Lee, Chin-Hui (Committee Chair), Clements, Mark (Committee Member), Lee, Hsien-Hsin (Committee Member), McClellan, James (Committee Member), Yuan, Ming (Committee Member).
Subjects/Keywords: Discriminative learning; Semi-supervised learning; Incremental learning; Image modeling; Multi-view learning; Machine learning; Supervised learning (Machine learning); Boosting (Algorithms)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Byun, B. (2012). On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43597
Chicago Manual of Style (16th Edition):
Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Doctoral Dissertation, Georgia Tech. Accessed January 17, 2021.
http://hdl.handle.net/1853/43597.
MLA Handbook (7th Edition):
Byun, Byungki. “On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling.” 2012. Web. 17 Jan 2021.
Vancouver:
Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/1853/43597.
Council of Science Editors:
Byun B. On discriminative semi-supervised incremental learning with a multi-view perspective for image concept modeling. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43597

University of Alberta
14.
Mahmood, Ashique.
Automatic step-size adaptation in incremental supervised
learning.
Degree: MS, Department of Computing Science, 2010, University of Alberta
URL: https://era.library.ualberta.ca/files/zc77sr03r
► Performance and stability of many iterative algorithms such as stochastic gradient descent largely depend on a fixed and scalar step-size parameter. Use of a fixed…
(more)
▼ Performance and stability of many iterative algorithms
such as stochastic gradient descent largely depend on a fixed and
scalar step-size parameter. Use of a fixed and scalar step-size
value may lead to limited performance in many problems. We study
several existing step-size adaptation algorithms in nonstationary,
supervised learning problems using simulated and real-world data.
We discover that effectiveness of the existing step-size adaptation
algorithms requires tuning of a meta parameter across problems. We
introduce a new algorithm - Autostep - by combining several new
techniques with an existing algorithm, and demonstrate that it can
effectively adapt a vector step-size parameter on all of our
training and test problems without tuning its meta parameter across
them. Autostep is the first step-size adaptation algorithm that can
be used in widely different problems with the same setting of all
of its parameters.
Subjects/Keywords: step size; supervised learning; stochastic gradient descent
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Mahmood, A. (2010). Automatic step-size adaptation in incremental supervised
learning. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/zc77sr03r
Chicago Manual of Style (16th Edition):
Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised
learning.” 2010. Masters Thesis, University of Alberta. Accessed January 17, 2021.
https://era.library.ualberta.ca/files/zc77sr03r.
MLA Handbook (7th Edition):
Mahmood, Ashique. “Automatic step-size adaptation in incremental supervised
learning.” 2010. Web. 17 Jan 2021.
Vancouver:
Mahmood A. Automatic step-size adaptation in incremental supervised
learning. [Internet] [Masters thesis]. University of Alberta; 2010. [cited 2021 Jan 17].
Available from: https://era.library.ualberta.ca/files/zc77sr03r.
Council of Science Editors:
Mahmood A. Automatic step-size adaptation in incremental supervised
learning. [Masters Thesis]. University of Alberta; 2010. Available from: https://era.library.ualberta.ca/files/zc77sr03r

Georgia Tech
15.
Ahsan, Unaiza.
Leveraging mid-level representations for complex activity recognition.
Degree: PhD, Interactive Computing, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/61199
► Dynamic scene understanding requires learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos…
(more)
▼ Dynamic scene understanding requires
learning representations of the components of the scene including objects, environments, actions and events. Complex activity recognition from images and videos requires annotating large datasets with action labels which is a tedious and expensive task. Thus, there is a need to design a mid-level or intermediate feature representation which does not require millions of labels, yet is able to generalize to semantic-level recognition of activities in visual data. This thesis makes three contributions in this regard. First, we propose an event concept-based intermediate representation which learns concepts via the Web and uses this representation to identify events even with a single labeled example. To demonstrate the strength of the proposed approaches, we contribute two diverse social event datasets to the community. We then present a use case of event concepts as a mid-level representation that generalizes to sentiment recognition in diverse social event images. Second, we propose to train Generative Adversarial Networks (GANs) with video frames (which does not require labels), use the trained discriminator from GANs as an intermediate representation and finetune it on a smaller labeled video activity dataset to recognize actions in videos. This unsupervised pre-training step avoids any manual feature engineering, video frame encoding or searching for the best video frame sampling technique. Our third contribution is a self-
supervised learning approach on videos that exploits both spatial and temporal coherency to learn feature representations on video data without any supervision. We demonstrate the transfer
learning capability of this model on smaller labeled datasets. We present comprehensive experimental analysis on the self-
supervised
model to provide insights into the unsupervised pretraining paradigm and how it can help with activity recognition on target datasets which the model has never seen during training.
Advisors/Committee Members: Essa, Irfan (advisor), Hays, James (committee member), De Choudhury, Munmun (committee member), Kira, Zsolt (committee member), Parikh, Devi (committee member), Sun, Chen (committee member).
Subjects/Keywords: Activity recognition; Self-supervised learning; Event recognition
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ahsan, U. (2019). Leveraging mid-level representations for complex activity recognition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61199
Chicago Manual of Style (16th Edition):
Ahsan, Unaiza. “Leveraging mid-level representations for complex activity recognition.” 2019. Doctoral Dissertation, Georgia Tech. Accessed January 17, 2021.
http://hdl.handle.net/1853/61199.
MLA Handbook (7th Edition):
Ahsan, Unaiza. “Leveraging mid-level representations for complex activity recognition.” 2019. Web. 17 Jan 2021.
Vancouver:
Ahsan U. Leveraging mid-level representations for complex activity recognition. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/1853/61199.
Council of Science Editors:
Ahsan U. Leveraging mid-level representations for complex activity recognition. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61199

McMaster University
16.
Ateeq, Sameen.
Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.
Degree: MSc, 2018, McMaster University
URL: http://hdl.handle.net/11375/24095
► According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in…
(more)
▼ According to the Public Health Agency of Canada, falls account for 95% of all hip fractures in Canada; 20% of fall-related injury cases end in death. This thesis evaluates the predictive power of many variables to predict fall-related injuries. The dataset chosen was CCHS which is high dimensional and diverse. The use of Principal Component Analysis (PCA) and random forest was employed to determine the highest priority risk factors to include in the predictive model. The results show that it is possible to predict fall-related injuries with a sensitivity of 80% or higher using four predictors (frequency of consultations with medical doctor, food and vegetable consumption, height and monthly physical activity level of over 15 minutes). Alternatively, the same sensitivity can be reached using age, frequency of walking for exercise per 3 months, alcohol consumption and personal income. None of the predictive models reached an accuracy of 70% or higher.
Further work in studying nutritional diets that offer protection from incurring a fall related injury are also recommended. Since the predictors are behavioral determinants of health and have a high sensitivity but a low accuracy, population health interventions are recommended rather than individual-level interventions. Suggestions to improve accuracy of built models are also proposed.
Thesis
Master of Science (MSc)
Advisors/Committee Members: Samavi, Reza, eHealth.
Subjects/Keywords: machine learning; supervised classification; falls; CCHS; injuries
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ateeq, S. (2018). Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/24095
Chicago Manual of Style (16th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Masters Thesis, McMaster University. Accessed January 17, 2021.
http://hdl.handle.net/11375/24095.
MLA Handbook (7th Edition):
Ateeq, Sameen. “Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries.” 2018. Web. 17 Jan 2021.
Vancouver:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/11375/24095.
Council of Science Editors:
Ateeq S. Machine Learning Approach on Evaluating Predictive Factors of Fall-Related Injuries. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/24095

Baylor University
17.
-1697-5430.
Semi-supervised learning for electrocardiography signal classification.
Degree: M.S.E.C.E., Baylor University. Dept. of Electrical & Computer Engineering., 2018, Baylor University
URL: http://hdl.handle.net/2104/10391
► An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected…
(more)
▼ An electrocardiogram (ECG) is a cardiology test that provides information about the structure and function of the heart. The size of the ECG data collected from patients can be very large, and the data analysis is tedious. Inspired by human
learning, in this thesis we propose a new semi-
supervised training framework for deep neural network to classify ECG data. The idea is to reward the valid associations that belong to the same class after a round trip during cross-matching of
supervised and unsupervised
learning, while penalizing the incorrect associations. The implementation of our framework can be easily integrated with any existing training setup. With data preprocessing, the detection of heart disease is improved.
Advisors/Committee Members: Dong, Liang, 1974- (advisor).
Subjects/Keywords: Semi-supervised learning; Electrocardiography; pattern recognition
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
-1697-5430. (2018). Semi-supervised learning for electrocardiography signal classification. (Masters Thesis). Baylor University. Retrieved from http://hdl.handle.net/2104/10391
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Masters Thesis, Baylor University. Accessed January 17, 2021.
http://hdl.handle.net/2104/10391.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
-1697-5430. “Semi-supervised learning for electrocardiography signal classification.” 2018. Web. 17 Jan 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Internet] [Masters thesis]. Baylor University; 2018. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/2104/10391.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
-1697-5430. Semi-supervised learning for electrocardiography signal classification. [Masters Thesis]. Baylor University; 2018. Available from: http://hdl.handle.net/2104/10391
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

University of Manchester
18.
Rostamniakankalhori, Sharareh.
Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.
Degree: 2011, University of Manchester
URL: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
► Tuberculosis (TB) is an infectious disease which is a global public health problem with over 9 million new cases annually. Tuberculosis treatment, with patient supervision…
(more)
▼ Tuberculosis (TB) is an infectious disease which is
a global public health problem with over 9 million new cases
annually. Tuberculosis treatment, with patient supervision and
support is an element of the global plan to stop TB designed by the
World Health Organization in 2006. The plan requires prediction of
patient treatment course destination. The prediction outcome can be
used to determine how intensive the level of supplying services and
supports in frame of DOTS therapy should be. No predictive model
for the outcome has been developed yet and only limited reports of
influential factors for considered outcome are available.To fill
this gap, this thesis develops a machine
learning approach to
predict the outcome of tuberculosis treatment course, which
includes, firstly, data of 6,450 Iranian TB patients under DOTS
(directly observed treatment, short course ) therapy were analysed
to initially diagnose the significant predictors by correlation
analysis; secondly, these significant features were applied to find
the best classification approach from six examined algorithms
including decision tree, Bayesian network, logistic regression,
multilayer perceptron, radial basis function, and support vector
machine; thirdly, the prediction accuracy of these existing
techniques was improved by proposing and developing a new
integrated method of k-mean clustering and classification
algorithms. Finally, a cluster-based simplified decision tree
(CSDT) was developed through an innovative hierarchical clustering
and classification algorithm. CSDT was built by k-mean partitioning
and the decision tree
learning. This innovative method not only
improves the prediction accuracy significantly but also leads to a
much simpler and interpretative decision tree.The main results of
this study included, firstly, finding seventeen significantly
correlated features which were: age, sex, weight, nationality, area
of residency, current stay in prison, low body weight, TB type,
treatment category, length of disease, TB case type, recent TB
infection, diabetic or HIV positive, and social risk factors like
history of imprisonment, IV drug usage, and unprotected sex ;
secondly, the results by applying and comparing six applied
supervised machine
learning tools on the testing set revealed that
decision trees gave the best prediction accuracy (74.21%) compared
with other methods; thirdly, by using testing set, the new
integrated approach to combine the clustering and classification
approach leads to the prediction accuracy improvement for all
applied classifiers; the most and least improvement for prediction
accuracy were shown by logistic regression (10%) and support vector
machine (4%) respectively. Finally, by applying the proposed and
developed CSDT, cluster-based simplified decision trees were
optioned, which reduced the size of the resulting decision tree and
further improved the prediction accuracy.Data type and having
normal distribution have created an opportunity for the decision
tree to outperform other algorithms. Pre-
learning by k-mean…
Advisors/Committee Members: Zeng, Xiaojun.
Subjects/Keywords: Integrated Supervised and Unsupervised Learning;
Tuberculosis; plediction
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Rostamniakankalhori, S. (2011). Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
Chicago Manual of Style (16th Edition):
Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.” 2011. Doctoral Dissertation, University of Manchester. Accessed January 17, 2021.
http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.
MLA Handbook (7th Edition):
Rostamniakankalhori, Sharareh. “Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course.” 2011. Web. 17 Jan 2021.
Vancouver:
Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. [Internet] [Doctoral dissertation]. University of Manchester; 2011. [cited 2021 Jan 17].
Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404.
Council of Science Editors:
Rostamniakankalhori S. Integrated Supervised and Unsupervised Learning Method to
Predict the Outcome of Tuberculosis Treatment Course. [Doctoral Dissertation]. University of Manchester; 2011. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:132404
19.
Zhao, Xuran.
Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.
Degree: Docteur es, Signal et images, 2013, Paris, ENST
URL: http://www.theses.fr/2013ENST0061
► Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments…
(more)
▼ Dans la plupart des systèmes biométriques de l’état de l’art, les données biométrique sont souvent représentés par des vecteurs de grande dimensionalité. La dimensionnalité d'éléments biométriques génèrent un problème de malédiction de dimensionnalité. Dans la biométrie multimodale, différentes modalités biométriques peuvent former différents entrés des algorithmes de classification. La fusion des modalités reste un problème difficile et est généralement traitée de manière isolée à celui de dimensionalité élevée. Cette thèse aborde le problème de la dimensionnalité élevée et le problème de la fusion multimodale dans un cadre unifié. En vertu d'un paramètre biométrique multi-modale et les données non étiquetées abondantes données, nous cherchons à extraire des caractéristiques discriminatoires de multiples modalités d'une manière non supervisée. Les contributions de cette thèse sont les suivantes: Un état de l’art des algorithmes RMVD de l'état de l'art ; Un nouveau concept de RMVD: accord de la structure de données dans sous-espace; Trois nouveaux algorithmes de MVDR basée sur des définitions différentes de l’accord de la structure dans les sous-espace; L’application des algorithmes proposés à la classification semi-supervisée, la classification non supervisée, et les problèmes de récupération de données biométriques, en particulier dans un contexte de la reconnaissance de personne en audio et vidéo; L’application des algorithmes proposés à des problèmes plus larges de reconnaissance des formes pour les données non biométriques, tels que l'image et le regroupement de texte et la recherche.
Biometric data is often represented by high-dimensional feature vectors which contain significant inter-session variation. Discriminative dimensionality reduction techniques generally follow a supervised learning scheme. However, labelled training data is generally limited in quantity and often does not reliably represent the inter-session variation encountered in test data. This thesis proposes to use multi-view dimensionality reduction (MVDR) which aims to extract discriminative features in multi-modal biometric systems, where different modalities are regarded as different views of the same data. MVDR projections are trained on feature-feature pairs where label information is not required. Since unlabelled data is easier to acquire in large quantities, and because of the natural co-existence of multiple views in multi-modal biometric problems, discriminant, low-dimensional subspaces can be learnt using the proposed MVDR approaches in a largely unsupervised manner. According to different functionalities of biometric systems, namely, clustering, and retrieval, we propose three MVDR frameworks which meet the requirements for each functionality. The proposed approaches, however, share the same spirit: all methods aim to learn a projection for each view such that a certain form of agreement is attained in the subspaces across different views. The proposed MVDR frameworks can thus be unified into one general framework for multi-view…
Advisors/Committee Members: Evans, Nicholas W. D. (thesis director), Dugelay, Jean-Luc (thesis director).
Subjects/Keywords: Apprentissage semi-supervisé; Semi-supervised learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhao, X. (2013). Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2013ENST0061
Chicago Manual of Style (16th Edition):
Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Doctoral Dissertation, Paris, ENST. Accessed January 17, 2021.
http://www.theses.fr/2013ENST0061.
MLA Handbook (7th Edition):
Zhao, Xuran. “Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics.” 2013. Web. 17 Jan 2021.
Vancouver:
Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Internet] [Doctoral dissertation]. Paris, ENST; 2013. [cited 2021 Jan 17].
Available from: http://www.theses.fr/2013ENST0061.
Council of Science Editors:
Zhao X. Réduction de la dimension multi-vue pour la biométrie multimodale : Multi-view dimensionality reduction for multi-modal biometrics. [Doctoral Dissertation]. Paris, ENST; 2013. Available from: http://www.theses.fr/2013ENST0061

Delft University of Technology
20.
Paramkusam, Deepak (author).
Comparison of Optimal Control Techniques for Learning-based RRT.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1
► Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring…
(more)
▼ Kinodynamic motion planning for a robot involves generating a trajectory from a given robot state to goal state while satisfying kinematic and dynamic constraints. Rapidly-exploring Random Trees (RRT) is a sampling-based algorithm that has been widely adopted for this. However, RRT is not fast enough to enable its use in industrial applications. Recently,
supervised learning has been used to pre-learn time consuming steps of RRT which resulted in improvement in planning times. The
supervised learning models require cost and control input of the system as training data which are generated using optimal control. The training data can be obtained either by indirect optimal control or direct optimal control techniques. In this thesis, both the techniques are each used to generate cost and control inputs for a two-link manipulator using random initial-final state pairs. Then each dataset is used to train a model and the datasets are compared based on certain training metrics. K-nearest neighbours regression and multi-layer perceptron neural network are the
supervised learning models used in this thesis. It is observed that both the datasets result in similar convergence of the models, but indirect optimal control approach allows upto 24-fold faster data generation and upto 3-fold reduction in dimensionality of training data compared to the direct optimal approach. Real-world robots have torque limits based on actuator configuration. The torque limits are modeled as control constraints in both the optimal control techniques and the effect of this restriction on data generation and
supervised learning is studied in this thesis. Direct optimal control is found to be better for data generation in this case due to the ease of applying control bounds as inequality constraints on the function approximations. Indirect optimal control is very tedious as active constraints should be known a priori to determine the switching points. An alternate method is explored instead where samples are generated similar to the unconstrained case but samples violating the constraints are removed. Poor control input
learning is observed in both approaches and the models struggled to extrapolate. It is hypothesised that this is due to inability of the constrained data to fully capture the system dynamics. However, good cost prediction is achieved using neural networks.
Advisors/Committee Members: Wisse, Martijn (mentor), Bharatheesha, Mukunda (mentor), Grammatico, Sergio (graduation committee), Wolfslag, Wouter (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: RRT; Supervised Learning; Optimal control; Motion Planning
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Paramkusam, D. (. (2018). Comparison of Optimal Control Techniques for Learning-based RRT. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1
Chicago Manual of Style (16th Edition):
Paramkusam, Deepak (author). “Comparison of Optimal Control Techniques for Learning-based RRT.” 2018. Masters Thesis, Delft University of Technology. Accessed January 17, 2021.
http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1.
MLA Handbook (7th Edition):
Paramkusam, Deepak (author). “Comparison of Optimal Control Techniques for Learning-based RRT.” 2018. Web. 17 Jan 2021.
Vancouver:
Paramkusam D(. Comparison of Optimal Control Techniques for Learning-based RRT. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 17].
Available from: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1.
Council of Science Editors:
Paramkusam D(. Comparison of Optimal Control Techniques for Learning-based RRT. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:742ed24e-0525-4ae2-b6d4-2dc6f69e60e1

Delft University of Technology
21.
Moring, Stefan (author).
Kinodynamic Steering using Supervised Learning in RRT.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a
► With the need for robots to operate autonomously increasing more and more, the research field of motion planning is becoming more active. Usually planning is…
(more)
▼ With the need for robots to operate autonomously increasing more and more, the research field of motion planning is becoming more active. Usually planning is done in configuration space, which often leads to non feasible solutions for highly dynamical or underactuated systems. With kinodynamic planning motion can also be planned for this difficult class of systems. However, due to the difficult nature of the problem, computation time is an issue. RRT CoLearn is a novel variant on the original RRT algorithm that tries to decrease computation time by replacing computational heavy steps in the algorithm with supervised learning. In this thesis the performance of RRT CoLearn is investigated, and it is found that it does not work on multi-DOF systems. Furthermore a novel steering function is presented called Inverse Dynamics Learning, which is shown to converge over five times faster than RRT CoLearn and also converge on a highly non-linear 2-DOF system.
Science in Signals & Systems
Advisors/Committee Members: Wisse, Martijn (mentor), Bharatheesha, Mukunda (mentor), Spaan, Matthijs (graduation committee), Alonso Mora, Javier (graduation committee), Moerland, Thomas (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: Motion Planning; RRT; Kinodynamic; Supervised Learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Moring, S. (. (2018). Kinodynamic Steering using Supervised Learning in RRT. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a
Chicago Manual of Style (16th Edition):
Moring, Stefan (author). “Kinodynamic Steering using Supervised Learning in RRT.” 2018. Masters Thesis, Delft University of Technology. Accessed January 17, 2021.
http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a.
MLA Handbook (7th Edition):
Moring, Stefan (author). “Kinodynamic Steering using Supervised Learning in RRT.” 2018. Web. 17 Jan 2021.
Vancouver:
Moring S(. Kinodynamic Steering using Supervised Learning in RRT. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Jan 17].
Available from: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a.
Council of Science Editors:
Moring S(. Kinodynamic Steering using Supervised Learning in RRT. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:fdf13674-f2b5-4b4a-b03d-21299114642a

University of Victoria
22.
Sharma, Mridula.
Evaluating and enhancing the security of cyber physical systems using machine learning approaches.
Degree: Department of Electrical and Computer Engineering, 2020, University of Victoria
URL: http://hdl.handle.net/1828/11675
► The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first…
(more)
▼ The main aim of this dissertation is to address the security issues of the physical layer of Cyber Physical Systems. The network security is first assessed using a 5-level Network Security Evaluation Scheme (NSES).
The network security is then enhanced using a novel Intrusion Detection System that is designed using
Supervised Machine
Learning. Defined as a complete architecture, this framework includes a complete packet analysis of radio traffic of Routing Protocol for Low-Power and Lossy Networks (RPL). A dataset of 300 different simulations of RPL network is defined for normal traffic, hello flood attack, DIS attack, increased version attack and decreased rank attack. The IDS is a multi-model detection model that provides an efficient detection against the known as well as new attacks.
The model analysis is done with the cross-validation method as well as using the new data from a similar network. To detect the known attacks, the model performed at 99% accuracy rate and for the new attack, 85% accuracy is achieved.
Advisors/Committee Members: Gebali, Fayez (supervisor), Elmiligi, Haytham (supervisor).
Subjects/Keywords: CPS; Supervised Machine Learning; RPL; Feature Selection
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sharma, M. (2020). Evaluating and enhancing the security of cyber physical systems using machine learning approaches. (Thesis). University of Victoria. Retrieved from http://hdl.handle.net/1828/11675
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Thesis, University of Victoria. Accessed January 17, 2021.
http://hdl.handle.net/1828/11675.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sharma, Mridula. “Evaluating and enhancing the security of cyber physical systems using machine learning approaches.” 2020. Web. 17 Jan 2021.
Vancouver:
Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Internet] [Thesis]. University of Victoria; 2020. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/1828/11675.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sharma M. Evaluating and enhancing the security of cyber physical systems using machine learning approaches. [Thesis]. University of Victoria; 2020. Available from: http://hdl.handle.net/1828/11675
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Notre Dame
23.
Troy William Raeder.
Evaluating and Maintaining Classification
Algorithms</h1>.
Degree: Computer Science and Engineering, 2012, University of Notre Dame
URL: https://curate.nd.edu/show/4b29b56616h
► Any practical application of machine learning necessarily begins with the selection of a classification algorithm. Generally, practitioners will try several different types of algorithms…
(more)
▼ Any practical application of machine
learning
necessarily begins with the selection of a classification
algorithm. Generally, practitioners will try several different
types of algorithms (such as decision trees, Bayesian algorithms,
support vector machines, or neural networks) and select the
algorithm that performs best on a subset of the available data.
That is to say, some measurement of the classifier’s performance on
past data is used as an estimate of its performance on future data.
Ideally, this estimate is perfectly aligned with the extit{true
cost} of applying the classifier on future data, but this far from
guaranteed in practice. First, any estimate of classifier
performance has variance, and this variance is difficult to
estimate. Additionally, misclassification costs are rarely known at
model-selection time and the characteristics of the population from
which data are drawn may change over time. If the training-time
estimate of either misclassification cost or data distribution is
incorrect, the chosen classifier is sub-optimal and may perform
worse than expected. Finally, once a suitable classifier is built
and deployed, there need to be systems in place to ensure that it
continues to perform at a high level over time. The purpose of this
dissertation is to improve the processes of classifier evaluation,
selection, and maintenance in real-world
situations.
Advisors/Committee Members: Dr. W. Philip Kegelmeyer, Committee Member, Dr. Patrick J. Flynn, Committee Member, Dr. Nitesh V. Chawla, Committee Chair, Dr. Kevin W. Bowyer, Committee Member.
Subjects/Keywords: classification; supervised learning; evaluation; concept drift
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Raeder, T. W. (2012). Evaluating and Maintaining Classification
Algorithms</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/4b29b56616h
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Thesis, University of Notre Dame. Accessed January 17, 2021.
https://curate.nd.edu/show/4b29b56616h.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Raeder, Troy William. “Evaluating and Maintaining Classification
Algorithms</h1>.” 2012. Web. 17 Jan 2021.
Vancouver:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Internet] [Thesis]. University of Notre Dame; 2012. [cited 2021 Jan 17].
Available from: https://curate.nd.edu/show/4b29b56616h.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Raeder TW. Evaluating and Maintaining Classification
Algorithms</h1>. [Thesis]. University of Notre Dame; 2012. Available from: https://curate.nd.edu/show/4b29b56616h
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Tennessee – Knoxville
24.
Amreen, Sadika.
Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.
Degree: 2019, University of Tennessee – Knoxville
URL: https://trace.tennessee.edu/utk_graddiss/5453
► Operational data from software development, social networks and other domains are often contaminated with incorrect or missing values. Examples include misspelled or changed names, multiple…
(more)
▼ Operational data from software development, social networks and other domains are often contaminated with incorrect or missing values. Examples include misspelled or changed names, multiple emails belonging to the same person and user profiles that vary in different systems. Such digital traces are extensively used in research and practice to study collaborating communities of various kinds. To achieve a realistic representation of the networks that represent these communities, accurate identities are essential. In this work, we aim to identify, model, and correct identity errors in data from open-source software repositories, which include more than 23M developer IDs and nearly 1B Git commits (developer activity records). Our investigation into the nature and prevalence of identity errors in software activity data reveals that they are different and occur at much higher rates than other domains. Existing techniques relying on string comparisons can only disambiguate Synonyms, but not Homonyms, which are common in software activity traces. Therefore, we introduce measures of behavioral fingerprinting to improve the accuracy of Synonym resolution, and to disambiguate Homonyms. Fingerprints are constructed from the traces of developers’ activities, such as, the style of writing in commit messages, the patterns in files modified and projects participated in by developers, and the patterns related to the timing of the developers’ activity. Furthermore, to address the lack of training data necessary for the supervised learning approaches that are used in disambiguation, we design a specific active learning procedure that minimizes the manual effort necessary to create training data in the domain of developer identity matching. We extensively evaluate the proposed approach, using over 16,000 OpenStack developers in 1200 projects, against commercial and most recent research approaches, and further on recent research on a much larger sample of over 2,000,000 IDs. Results demonstrate that our method is significantly better than both the recent research and commercial methods. We also conduct experiments to demonstrate that such erroneous data have significant impact on developer networks. We hope that the proposed approach will expedite research progress in the domain of software engineering, especially in applications for which graphs of social networks are critical.
Subjects/Keywords: identity disambiguation; supervised learning; behavioral fingerprinting
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Amreen, S. (2019). Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5453
Chicago Manual of Style (16th Edition):
Amreen, Sadika. “Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.” 2019. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed January 17, 2021.
https://trace.tennessee.edu/utk_graddiss/5453.
MLA Handbook (7th Edition):
Amreen, Sadika. “Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data.” 2019. Web. 17 Jan 2021.
Vancouver:
Amreen S. Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2019. [cited 2021 Jan 17].
Available from: https://trace.tennessee.edu/utk_graddiss/5453.
Council of Science Editors:
Amreen S. Methods of Disambiguating and De-anonymizing Authorship in Large Scale Operational Data. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_graddiss/5453

University of North Texas
25.
Dandala, Bharath.
Multilingual Word Sense Disambiguation Using Wikipedia.
Degree: 2013, University of North Texas
URL: https://digital.library.unt.edu/ark:/67531/metadc500036/
► Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in…
(more)
▼ Ambiguity is inherent to human language. In particular, word sense ambiguity is prevalent in all natural languages, with a large number of the words in any given language carrying more than one meaning. Word sense disambiguation is the task of automatically assigning the most appropriate meaning to a polysemous word within a given context. Generally the problem of resolving ambiguity in literature has revolved around the famous quote “you shall know the meaning of the word by the company it keeps.” In this thesis, we investigate the role of context for resolving ambiguity through three different approaches. Instead of using a predefined monolingual sense inventory such as WordNet, we use a language-independent framework where the word senses and sense-tagged data are derived automatically from Wikipedia. Using Wikipedia as a source of sense-annotations provides the much needed solution for knowledge acquisition bottleneck. In order to evaluate the viability of Wikipedia based sense-annotations, we cast the task of disambiguating polysemous nouns as a monolingual classification task and experimented on lexical samples from four different languages (viz. English, German, Italian and Spanish). The experiments confirm that the Wikipedia based sense annotations are reliable and can be used to construct accurate monolingual sense classifiers. It is a long belief that exploiting multiple languages helps in building accurate word sense disambiguation systems. Subsequently, we developed two approaches that recast the task of disambiguating polysemous nouns as a multilingual classification task. The first approach for multilingual word sense disambiguation attempts to effectively use a machine translation system to leverage two relevant multilingual aspects of the semantics of text. First, the various senses of a target word may be translated into different words, which constitute unique, yet highly salient signal that effectively expand the target word’s feature space. Second, the translated context words themselves embed co-occurrence information that a translation engine gathers from very large parallel corpora. The second approach for multlingual word sense disambiguation attempts to reduce the reliance on the machine translation system during training by using the multilingual knowledge available in Wikipedia through its interlingual links. Finally, the experiments on a lexical sample from four different languages confirm that the multilingual systems perform better than the monolingual system and significantly improve the disambiguation accuracy.
Advisors/Committee Members: Mihalcea, Rada, 1974-, Tarau, Paul, Nielsen, Rodney, Bunescu, Răzvan.
Subjects/Keywords: Wikipedia; word sense disambiguation; supervised learning; multilingual
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Penn State University
26.
Biyani, Prakhar.
Analyzing Subjectivity and Sentiment of Online Forums.
Degree: 2014, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/22850
► Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs…
(more)
▼ Online social media has emerged as a popular medium for seeking and providing information, opinions and social support. Online sites such as discussion forums, blogs and health communities have tremendous amounts of user generated data in their archives. Analyzing this content for its subjectivity and sentiment has important applications such as improving information search in social media, understanding users for providing content personalization, identifying influential members in online communities, etc. In this dissertation, I will discuss my works on subjectivity analysis of online forum threads, identifying the type of social support (emotional or informational) present in and analyzing sentiment of user messages in an online health community (OHC). For subjectivity analysis, I show that thread-specific non-lexical features such as thread structure and dialogue acts expressed in thread posts are highly informative for inferring thread subjectivity. For sentiment analysis of messages of the OHC, I use unlabeled messages to augment a small training data using co-training and build highly accurate sentiment classifiers. For support identification, I build
supervised classifiers using several generic and novel domain-specific features and analyze the posting behaviors of regular members and influential members in the OHC in terms of the type of support they provide in their messages. I find that influential members generally provide more emotional support as compared to regular members in the OHC. Experimental results demonstrate that all the proposed models significantly outperform various state-of-the-art models.
Advisors/Committee Members: Prasenjit Mitra, Dissertation Advisor/Co-Advisor, Prasenjit Mitra, Committee Chair/Co-Chair, John Yen, Committee Member, Alexander Klippel, Committee Member, Marcel Salathe, Committee Member, Cornelia Caragea, Special Member.
Subjects/Keywords: Subjectivity analysis; sentiment analysis; classification; supervised learning; semi-supervised learning; online forums
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Biyani, P. (2014). Analyzing Subjectivity and Sentiment of Online Forums. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/22850
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Thesis, Penn State University. Accessed January 17, 2021.
https://submit-etda.libraries.psu.edu/catalog/22850.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Biyani, Prakhar. “Analyzing Subjectivity and Sentiment of Online Forums.” 2014. Web. 17 Jan 2021.
Vancouver:
Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Internet] [Thesis]. Penn State University; 2014. [cited 2021 Jan 17].
Available from: https://submit-etda.libraries.psu.edu/catalog/22850.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Biyani P. Analyzing Subjectivity and Sentiment of Online Forums. [Thesis]. Penn State University; 2014. Available from: https://submit-etda.libraries.psu.edu/catalog/22850
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Oregon State University
27.
Hao, Guohua.
Efficient training and feature induction in sequential supervised learning.
Degree: PhD, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/12548
► Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature…
(more)
▼ Sequential
supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential
supervised learning: efficient training and feature induction.
In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential
supervised learning problems. Existing training algorithms for CRFs are slow, particularly in problems with large numbers of potential input features and feature combinations. In this dissertation, we describe a new algorithm, TreeCRF, for training CRFs via gradient tree boosting. In TreeCRF, the CRF potential functions are represented as weighted sums of regression trees, which provide compact representations of feature interactions. So the algorithm does not explicitly consider the potentially large parameter space. As a result, gradient tree boosting scales linearly in the order of the Markov model and in the order of the feature interactions, rather than exponentially as in previous algorithms based on iterative scaling and gradient descent. Detailed experimental results are provided to evaluate the performance of the TreeCRF algorithm and possible extensions of this algorithm are discussed.
We also study the problem of handling missing input values in CRFs, which has been rarely discussed in the literature. Gradient tree boosting also makes it possible to use instance weighting (as in C4.5) and surrogate splitting (as in CART) to handle missing values in CRFs. Experimental studies of the effectiveness of these two methods (as well as standard imputation and indicator feature methods) show that instance weighting is the best method in most cases when feature values are missing at random.
In the direction of feature induction, we study the search-based structured
learning framework and its application to sequential
supervised learning problems. By formulating the label sequence prediction process as an incremental search process from one end of a sequence to the other, this framework is able to avoid complicated inference algorithms in the training process and thus achieves very fast training speed. However, for problems where there exist long range dependencies between the current position and future positions, at each search step, this framework is unable to exploit these dependencies to make accurate predictions. In this dissertation, a multiple-instance
learning based algorithm is proposed to automatically extract useful features from future positions as a way to discover and exploit these long range dependencies. Integrating this algorithm with maximum entropy Markov models yields promising experimental results on both synthetic data sets and real data sets that have long range dependencies in sequences.
Advisors/Committee Members: Dietterich, Thomas G. (advisor), Fern, Alan (committee member).
Subjects/Keywords: Machine Learning; Supervised learning (Machine learning) – Mathematical models
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hao, G. (2009). Efficient training and feature induction in sequential supervised learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12548
Chicago Manual of Style (16th Edition):
Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed January 17, 2021.
http://hdl.handle.net/1957/12548.
MLA Handbook (7th Edition):
Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 17 Jan 2021.
Vancouver:
Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/1957/12548.
Council of Science Editors:
Hao G. Efficient training and feature induction in sequential supervised learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12548

Linköping University
28.
Alirezaie, Marjan.
Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.
Degree: Computer and Information Science, 2011, Linköping University
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
► The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem…
(more)
▼ The present thesis addresses machine learning in a domain of naturallanguage phrases that are names of universities. It describes two approaches to this problem and a software implementation that has made it possible to evaluate them and to compare them.
In general terms, the system's task is to learn to 'understand' the significance of the various components of a university name, such as the city or region where the university is located, the scienti c disciplines that are studied there, or the name of a famous person which may be part of the university name. A concrete test for whether the system has acquired this understanding is when it is able to compose a plausible university name given some components that should occur in the name.
In order to achieve this capability, our system learns the structure of available names of some universities in a given data set, i.e. it acquires a grammar for the microlanguage of university names. One of the challenges is that the system may encounter ambiguities due to multi meaning words. This problem is addressed using a small ontology that is created during the training phase.
Both domain knowledge and grammatical knowledge is represented using decision trees, which is an ecient method for concept learning. Besides for inductive inference, their role is to partition the data set into a hierarchical structure which is used for resolving ambiguities.
The present report also de nes some modi cations in the de nitions of parameters, for example a parameter for entropy, which enable the system to deal with cognitive uncertainties. Our method for automatic syntax acquisition, ADIOS, is an unsupervised learning method. This method is described and discussed here, including a report on the outcome of the tests using our data set.
The software that has been implemented and used in this project has been implemented in C.
Subjects/Keywords: Machine Learning; Supervised Learning; Unsupervised Learning; Computer Sciences; Datavetenskap (datalogi)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Alirezaie, M. (2011). Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Thesis, Linköping University. Accessed January 17, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Alirezaie, Marjan. “Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation.” 2011. Web. 17 Jan 2021.
Vancouver:
Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Internet] [Thesis]. Linköping University; 2011. [cited 2021 Jan 17].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Alirezaie M. Semantic Analysis Of Multi Meaning Words Using Machine Learning And Knowledge Representation. [Thesis]. Linköping University; 2011. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-70086
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Delft University of Technology
29.
Jurasiński, Karol (author).
Towards deeper understanding of semi-supervised learning with variational autoencoders.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
► Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled…
(more)
▼ Recently, deep generative models have been shown to achieve state-of-the-art performance on semi-
supervised learning tasks. In particular, variational autoencoders have been adopted to use labeled data, which allowed the development of SSL models with the usage of deep neural networks. However, some of these models rely on ad-hoc loss additions for training, and have constraints on the latent space, which effectively prevent the use of recent developments in improving the posterior approximations. In this paper, we analyse the limitations of semi-
supervised deep generative models based on VAEs, and show that it is possible to drop the assumptions made on the latent space. We present a simplified method for semi-
supervised learning which combines the discriminative and generative loss in a principled manner. Our model allows for straightforward application of normalizing flows and achieves competitive results in semi-
supervised classification tasks.
Advisors/Committee Members: Loog, Marco (mentor), Viering, Tom (mentor), Hung, Hayley (graduation committee), Verwer, Sicco (graduation committee), Delft University of Technology (degree granting institution).
Subjects/Keywords: semi-supervised learning; variational inference; deep learning; machine learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jurasiński, K. (. (2019). Towards deeper understanding of semi-supervised learning with variational autoencoders. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
Chicago Manual of Style (16th Edition):
Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Masters Thesis, Delft University of Technology. Accessed January 17, 2021.
http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.
MLA Handbook (7th Edition):
Jurasiński, Karol (author). “Towards deeper understanding of semi-supervised learning with variational autoencoders.” 2019. Web. 17 Jan 2021.
Vancouver:
Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Jan 17].
Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb.
Council of Science Editors:
Jurasiński K(. Towards deeper understanding of semi-supervised learning with variational autoencoders. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:e92c56a8-72a6-48d2-9205-a78cbc889ffb
30.
WALLHEDE, ERIK.
Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
.
Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2019, Chalmers University of Technology
URL: http://hdl.handle.net/20.500.12380/301903
► Artificial intelligence research is currently a hot topic within many industries. In terms of research, games such as StarCraft II provide a good testing ground…
(more)
▼ Artificial intelligence research is currently a hot topic within many industries. In
terms of research, games such as StarCraft II provide a good testing ground due to
its accessibility. However, getting started can still be more difficult than it should
be.
This paper aims to facilitate the development of a machine learning agent for
StarCraft II by designing tools for data collection, making a simple API built on
top of PySC2 to facilitate interaction with the game and by analyzing a few different
types of artificial neural networks with respect to StarCraft II.
It is concluded that defining reward functions for reinforcement learning can give
rise to unexpected behaviors. A further conclusion is that convolutional neural
networks tend to be more resource intensive than non-convolutional networks and
that they are thus less suited for anyone without access to large computational
power. Lastly, a network is trained on collected data to continuously predict the
win chance for players in a StarCraft II match. Unfortunately the network does not
become successful in its task, likely in part due to the simplicity of the network.
Subjects/Keywords: Artificial Neural Networks;
Machine Learning;
StarCraft II;
Reinforcement learning;
Supervised learning
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
WALLHEDE, E. (2019). Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
. (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301903
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
WALLHEDE, ERIK. “Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
.” 2019. Thesis, Chalmers University of Technology. Accessed January 17, 2021.
http://hdl.handle.net/20.500.12380/301903.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
WALLHEDE, ERIK. “Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
.” 2019. Web. 17 Jan 2021.
Vancouver:
WALLHEDE E. Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
. [Internet] [Thesis]. Chalmers University of Technology; 2019. [cited 2021 Jan 17].
Available from: http://hdl.handle.net/20.500.12380/301903.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
WALLHEDE E. Machine Learning in StarCraft II - Lowering the Difficulty Threshold of Starting From Scratch
. [Thesis]. Chalmers University of Technology; 2019. Available from: http://hdl.handle.net/20.500.12380/301903
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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